class_docstr: Adds TensorFlow specific functions.
docstr: Initialize the (Tensorflow specific parts) of the backend engine.
  
  There are not really tensorflow specifics besides the import, but don't
  forget to set up the optimizers after the init!
docstr_tok: ["Initialize","the","(","Tensorflow","specific","parts",")","of","the","backend","engine",".","There","are","not","really","tensorflow","specifics","besides","the","import",",","but","don't","forget","to","set","up","the","optimizers","after","the","init","!"]
code: def __init__(self, base, X, Y, A):
      """
      Initialize the (Tensorflow specific parts) of the backend engine.
  
      There are not really tensorflow specifics besides the import, but don't
      forget to set up the optimizers after the init!
      """
      global tensorflow
      import tensorflow
  
      tensorflow.random.set_seed(base.random_state_.random())
  
      self.model_class = tensorflow.keras.Model
      self.optim_class = tensorflow.keras.optimizers.Optimizer
      super(TensorflowEngine, self).__init__(base, X, Y, A)
  
file_path: fairlearn/fairlearn/adversarial/_tensorflow_engine.py
imports: from ._backend_engine import BackendEngine
  from numpy import finfo, float32
label_desc: Use image node_id 1 to create a new TensorflowEngine object from inherited base classes: BackendEngine with example: obj = TensorflowEngine(base, X, Y, A)